Spatial Role Labeling Annotation Scheme

Kordjamshidi, Parisa; Otterlo, Martijn van; Moens, Marie‐Francine

doi:10.1007/978-94-024-0881-2_38

Cited by 33 publications

(47 citation statements)

References 32 publications

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“…These efforts start from defining linguistic annotation schemes (Pustejovsky and Moszkowicz, 2008;Kordjamshidi et al, 2010;Pustejovsky and Moszkowicz, 2012;jeet Mani, 2009), annotating data and defining tasks (Kordjamshidi et al, 2012;Oleksandr Kolomiyets and Bethard, 2013;Pustejovsky et al, 2015) to operate on the annotated corpora and learn extraction models. However, there exists, yet, a large gap between the current models and the ones that can perform reasonably well in practice for real world applications in various domains.…”

Section: Related Researchmentioning

confidence: 99%

“…One approach towards spatial language understanding is to map the natural language to a formal spatial representation appropriate for spatial reasoning. The previous research on spatial role labeling (Kordjamshidi et al, 2010(Kordjamshidi et al, , 2017b(Kordjamshidi et al, , 2012 and ISOSpace (Pustejovsky et al, 2011(Pustejovsky et al, , 2015 aimed at formalizing such a problem and providing machine learning solutions to find such a mapping in a data-driven way Kordjamshidi et al, 2011). Such extractions are made from available textual resources.…”

Section: Introductionmentioning

confidence: 99%

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Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing

2017

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ii Introduction Welcome to the Second workshop on structured prediction for NLP! Many prediction tasks in NLP involve assigning values to mutually dependent variables. For example, when designing a model to automatically perform linguistic analysis of a sentence or a document (e.g., parsing, semantic role labeling, or discourse analysis), it is crucial to model the correlations between labels. Many other NLP tasks, such as machine translation, textual entailment, and information extraction, can be also modeled as structured prediction problems.In order to tackle such problems, various structured prediction approaches have been proposed, and their effectiveness has been demonstrated. Studying structured prediction is interesting from both NLP and machine learning (ML) perspectives. From the NLP perspective, syntax and semantics of the natural language are clearly structured and advances in this area will enable researchers to understand the linguistic structure of data. From the ML perspective, a large amount of available text data and complex linguistic structures bring challenges to the learning community. Designing expressive yet tractable models and studying efficient learning and inference algorithms become important issues.Recently, there has been significant interest in non-standard structured prediction approaches that take advantage of non-linearity, latent components, and/or approximate inference in both the NLP and ML communities. Researchers have also been discussing the intersection between deep learning and structured prediction through the DeepStructure reading group. This workshop intends to bring together NLP and ML researchers working on diverse aspects of structured prediction and expose the participants to recent progress in this area.This year we have eight papers covering various aspects of structured prediction, including neural networks, deep structured prediction, and imitation learning. We also invited four fantastic speakers. We hope you all enjoy the program! Finally, we would like to thank all programming committee members, speakers, and authors. We are looking forward to seeing you in Copenhagen. AbstractDependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale. Recent advances in GPU hardware have enabled neural networks to achieve significant gains over the previous best models, these models still fail to leverage GPUs' capability for massive parallelism due to their requirement of sequential processing of the sentence. In response, we propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for graphbased dependency parsing, a graph convolutional architecture that allows for efficient end-to-end GPU parsing. In experiments on the English Penn TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best neural network parsers.

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Section: Related Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing

2017

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“…We aim at learning to assign the segments in the sentence to the concepts in the ontology. The considered concepts form a light weight ontology which is based on a previously proposed spatial annotation scheme by the authors [1]. We highlight the distinction between two spatial role labeling (SpRL) and spatial qualitative labeling (SpQL) layers in the ontology.…”

Section: General Problem Definitionmentioning

confidence: 99%

“…The first contribution is that we propose a spatial ontology based on two layers of semantics. This ontology is based on a previously proposed spatial annotation scheme by the authors [1]. Its first layer is based on commonly accepted cognitive spatial notions and the second is based on multiple well-known qualitative spatial reasoning models.…”

Section: Introductionmentioning

confidence: 99%

Global Machine Learning for Spatial Ontology Population

Kordjamshidi

Moens

2015

SSRN Journal

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Understanding spatial language is important in many applications such as geographical information systems, human computer interaction or text-to-scene conversion. Due to the challenges of designing spatial ontologies, the extraction of spatial information from natural language still has to be placed in a well-defined framework. In this work, we propose an ontology which bridges between cognitive-linguistic spatial concepts in natural language and multiple qualitative spatial representation and reasoning models. To make a mapping between natural language and the spatial ontology, we propose a novel global machine learning framework for ontology population. In this framework we consider relational features and background knowledge which originates from both ontological relationships between the concepts and the structure of the spatial language. The advantage of the proposed global learning model is the scalability of the inference, and the flexibility for automatically describing text with arbitrary semantic labels that form a structured ontological representation of its content. The machine learning framework is evaluated with SemEval-2012 and SemEval-2013 data from the spatial role labeling task.

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“…For a QSLink or OLink relation, on the other hand, we think that attributes trajector and landmark can be seen as arguments of the predicate that dominates the trigger, which is usually a preposition (spatial signal). The idea behind these attributes is based on the Spatial Role Labeling annotation schema described in (Kordjamshidi et al, 2010). According to this scheme there are indicators that can be spatial (spatial signals) or of motion (words of motion) that introduce spatial relations.…”

Section: Attribute Identificationmentioning

confidence: 99%